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Cow estrus detection via Discrete Wavelet Transformation and Unsupervised Clustering

Published: 06 December 2018 Publication History

Abstract

Estrus is a special periods in the life cycle of female cows. Within this period, they have much more chance to become pregnant. Successfully detecting this period increase the milk and meat productivity of the whole farm. Recently, a potential approach is unsupervised learning on motion data of the cows, similar to human activity recognition based on motion. In particular, an accelerometer is attached to the neck of the cows to measure their acceleration, then the unsupervised algorithm group the measured acceleration time-series. Recent study adopted bag-of-feature and Discrete Fourier Transform for feature extraction, yet it may not reflect the nature of motion data. Thus, we proposed a method based on Discrete Wavelet Transform to get the multi-resolution feature, Dynamic Time Wraping as clustering distance and Iterative-K-Means as clustering algorithm, to better match with the characteristic of cowsâĂŹ movement. The proposed methods demonstrated higher score on human activity recognition dataset with ground truth and more reliable prediction on cow motion dataset.

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Cited By

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  • (2025)Comparative analysis of unsupervised anomaly detection techniques for heat detection in dairy cattleNeurocomputing10.1016/j.neucom.2024.129088618(129088)Online publication date: Feb-2025
  • (2024)Evaluation of One-Class Techniques for Early Estrus Detection on Galician Intensive Dairy Cow Farm Based on Behavioral Data From Activity CollarsADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal10.14201/adcaij.3250813(e32508)Online publication date: 31-Dec-2024
  • (2024)Real-time behavior recognition of animal: an IoT-based system design using acceleration dataMultimedia Tools and Applications10.1007/s11042-024-20309-5Online publication date: 7-Oct-2024
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  1. Cow estrus detection via Discrete Wavelet Transformation and Unsupervised Clustering

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    cover image ACM Other conferences
    SoICT '18: Proceedings of the 9th International Symposium on Information and Communication Technology
    December 2018
    496 pages
    ISBN:9781450365390
    DOI:10.1145/3287921
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    • SOICT: School of Information and Communication Technology - HUST
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    Published: 06 December 2018

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    Author Tags

    1. cow estrus detection
    2. discrete wavelet transformation
    3. dynamic time wrapping
    4. unsupervised learning

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    Cited By

    View all
    • (2025)Comparative analysis of unsupervised anomaly detection techniques for heat detection in dairy cattleNeurocomputing10.1016/j.neucom.2024.129088618(129088)Online publication date: Feb-2025
    • (2024)Evaluation of One-Class Techniques for Early Estrus Detection on Galician Intensive Dairy Cow Farm Based on Behavioral Data From Activity CollarsADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal10.14201/adcaij.3250813(e32508)Online publication date: 31-Dec-2024
    • (2024)Real-time behavior recognition of animal: an IoT-based system design using acceleration dataMultimedia Tools and Applications10.1007/s11042-024-20309-5Online publication date: 7-Oct-2024
    • (2022)Hareketlilik ve Çevre Verileri Kullanılarak Yapay Sinir Ağları ile Sığırlarda Kızgınlık TespitiJournal of Agricultural Faculty of Gaziosmanpasa University10.55507/gopzfd.111615539:1(40-45)Online publication date: 30-Apr-2022
    • (2022)An IoT-Based Design Using Accelerometers in Animal Behavior Recognition SystemsIEEE Sensors Journal10.1109/JSEN.2021.305119422:18(17515-17528)Online publication date: 15-Sep-2022
    • (2021)Development of Activity Collecting System for Grazing Cattle in Vast Land広域放牧牛のための飼育モニタリングシステムの開発IEEJ Transactions on Electronics, Information and Systems10.1541/ieejeiss.141.281141:3(281-287)Online publication date: 1-Mar-2021
    • (2021)Development of activity collecting system for grazing cattle in vast landElectronics and Communications in Japan10.1002/ecj.12314104:2Online publication date: 4-May-2021
    • (2019)Cow estrus detection with low-frequency accelerometer sensor by unsupervised learningProceedings of the 10th International Symposium on Information and Communication Technology10.1145/3368926.3369683(342-349)Online publication date: 4-Dec-2019

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